Machine Learning, Volume 29, 1997

Volume 29, Number 1, October 1997

• Paul E. Utgoff, Neil C. Berkman, Jeffery A. Clouse:
  Decision Tree Induction Based on Efficient Tree Restructuring. 5-44
  
• Shai Ben-David, Eyal Kushilevitz, Yishay Mansour:
  Online Learning versus Offline Learning. 45-63
  
• Kenneth Basye, Thomas Dean, Jeffrey Scott Vitter:
  Coping with Uncertainty in Map Learning. 65-88
  

Volume 29, Numbers 2-3, November 1997

• Pat Langley, Gregory M. Provan, Padhraic Smyth:
  Learning with Probabilistic Representations. 91-101
  
• Pedro Domingos, Michael J. Pazzani:
  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. 103-130
  
• Nir Friedman, Dan Geiger, Moisés Goldszmidt:
  Bayesian Network Classifiers. 131-163
  
• Sanjoy Dasgupta:
  The Sample Complexity of Learning Fixed-Structure Bayesian Networks. 165-180
  
• David Maxwell Chickering, David Heckerman:
  Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables. 181-212
  
• John Binder, Daphne Koller, Stuart J. Russell, Keiji Kanazawa:
  Adaptive Probabilistic Networks with Hidden Variables. 213-244
  
• Zoubin Ghahramani, Michael I. Jordan:
  Factorial Hidden Markov Models. 245-273
  
• Naoki Abe, Hiroshi Mamitsuka:
  Predicting Protein Secondary Structure Using Stochastic Tree Grammars. 275-301
  

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